Researchers in the Georgia Tech Research Institute (GTRI) and the Georgia Tech School of Electrical and Computer Engineering are developing programming tools to enable engineers in the defense industry to utilize the processing power of GPUs without having to learn the complicated programming language required to use them directly.
Mark Richards, a principal research engineer and adjunct professor in the School of Electrical and Computer Engineering, is collaborating with Campbell and graduate student Andrew Kerr to rewrite common signal processing commands to run on a GPU. This work is supported by the U.S. Defense Advanced Research Projects Agency and the U.S. Air Force Research Laboratory.
The researchers are currently writing the functions in Nvidia's CUDATM language, but the underlying principles can be applied to GPUs developed by other companies, according to Campbell. With GPU VSIPL, engineers can use high-level functions in their C programs to perform linear algebra and signal processing operations, and recompile with GPU VSIPL to take advantage of the speed of the GPU. Studies have shown that VSIPL functions operate between 20 and 350 times faster on a GPU than a central processing unit, depending on the function and size of the data set.
The research team is also assessing the advantages of GPUs by running a library of benchmarks for quantitatively comparing high-performance, embedded computing systems. The benchmarks address important operations across a broad range of U.S. Department of Defense signal and image processing applications.
For the future, the researchers plan to continue expanding the GPU VSIPL, develop additional defense-related GPU function libraries and design programming tools to utilize other efficient processors, such as the cell broadband engine processor at the heart of the PlayStation 3 video game console.
Mark Richards, a principal research engineer and adjunct professor in the School of Electrical and Computer Engineering, is collaborating with Campbell and graduate student Andrew Kerr to rewrite common signal processing commands to run on a GPU. This work is supported by the U.S. Defense Advanced Research Projects Agency and the U.S. Air Force Research Laboratory.The researchers are currently writing the functions in Nvidia's CUDATM language, but the underlying principles can be applied to GPUs developed by other companies, according to Campbell. With GPU VSIPL, engineers can use high-level functions in their C programs to perform linear algebra and signal processing operations, and recompile with GPU VSIPL to take advantage of the speed of the GPU. Studies have shown that VSIPL functions operate between 20 and 350 times faster on a GPU than a central processing unit, depending on the function and size of the data set.
The research team is also assessing the advantages of GPUs by running a library of benchmarks for quantitatively comparing high-performance, embedded computing systems. The benchmarks address important operations across a broad range of U.S. Department of Defense signal and image processing applications.For the future, the researchers plan to continue expanding the GPU VSIPL, develop additional defense-related GPU function libraries and design programming tools to utilize other efficient processors, such as the cell broadband engine processor at the heart of the PlayStation 3 video game console.
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